www.HIMSSConference.org
#smartHIT
FEBRUARY 11, 2019
ORLANDO, FL
How AI Enabled a Community
Hospital to Tackle Clinical Variation
And Reduce Length-of-Stay
Topics I will cover
Why use AI for Managing Clinical Variation
Topics I will cover
What we at Flagler Hospital have done
Topics I will cover
How we operationalized it
Topics I will cover
Our Results
Why reducing Clinical Variation Is Important
$750+ B wasted on unnecessary care
Why reducing Clinical Variation Is Important
Medical mistakes are now the 3
rd
leading cause of
death in the USA
Why reducing Clinical Variation Is Important
Now the 3
rd
leading cause of death in Canada
Why reducing Clinical Variation Is Important
WHO reports 95,000 deaths in Europe due to
Medical Errors (15
th
?)
2012 Institute of Medicine
67%
30%
3%
Percent
Necessary
Unnecessary
Deaths
$750 B
Variation in Healthcare Expenditures 2011 AHA
Unexplained
Types of Variation
Common Cause Variation
Special Cause Variation
Types of Variation
Common Cause Variation
It is always present
Special Cause Variation
Types of Variation
Common Cause Variation
It is inherent in the process
Special Cause Variation
Types of Variation
Common Cause Variation
To change the results, change the process
Special Cause Variation
Types of Variation
Common Cause Variation
Special Cause Variation
It does not always happen in the process
Types of Variation
Common Cause Variation
Special Cause Variation
Since it is so different, you would want to ask “why”
Types of Variation
Common Cause Variation
Special Cause Variation
Eliminate or minimize the impact if negative
Types of Variation
Common Cause Variation
Special Cause Variation
Not knowing the difference will create wasted time & effort
Hospitals have tried for years to reduce clinical variation
We gather the data we think is important
Hospitals have tried for years to reduce clinical variation
We analyze the data then try to implement our findings
Hospitals have tried for years to reduce clinical variation
By the time we act, the data may have changed significantly
Over the past several years, three things have changed
We now have massive computational power
Over the past several years, three things have changed
Our EMR has massive amounts of data
Over the past several years, three things have changed
AI systems like Ayasdi can now look at all the data and answer
questions we did not know to ask.
Methods of AI
Methods of AI
Supervised Learning
Methods of AI
Unsupervised Learning
Methods of AI
Ayasdi uses unsupervised learning and a branch of
mathematics called Topology
Methods of AI
Euler, in 1736 solution to the 7 bridges of Konigsberg
Topological Map of our Pneumonia Pilot
Or Team
What did we need to do?
2,500 lines of SQL code to extract the data
What did we need to do?
Upload to Ayasdi
What did we need to do?
Perform Semantic and Syntactic Validation
What did we need to do?
Generate the Treatment Groups
What did we need to do?
Select the “Goldilocks” Cohort
What did we need to do?
Ayasdi generates the CarePath GL Cohort
What did we need to do?
Begin monitoring our providers
61.1%
8.9%
14.8%
22.3%
9.8%
28.9%
20.0%
13.3%
38.8%
31.5%
24.1%
29.1%
36.6%
23.7%
25.7%
33.3%
31.0%
12.1%
18.5%
16.5%
24.4%
34.2%
25.7%
33.3%
2.3%
6.5%
4.6%
7.8%
7.3%
0.0% 0.0%
3.3%
0.0%
1.6%
1.9%
1.0%
0.0% 0.0%
2.9%
0.0%
0%
10%
20%
30%
40%
50%
60%
70%
Cohort 216 Cohort 124 Cohort 108 Cohort 103 Cohort 41 Cohort 38 Cohort 35 Cohort 30
Comorbid Conditions
Pneumonia
Diabetes COPD CHF Hypotension Mortality
3.99
3.35
3.04
3.83
2.90
3.11
2.11
3.37
$3.17
$2.46
$2.32
$2.95
$2.42
$2.68
$2.01
$2.76
0.9%
0.0%
0.9%
0.0% 0.0% 0.0%
2.9%
0.0%
0.0%
1.6%
1.9%
1.0%
0.0% 0.0%
2.9%
0.0%
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
Cohort 216 Cohort 124 Cohort 108 Cohort 103 Cohort 41 Cohort 38 Cohort 35 Cohort 30
Cohort Evaluation
Pneumonia
LOS DVC/k Readmit Mortality
12.30
3.49
4.91
2.89
$13.05
$4.21
$5.10
$3.21
1.1%
0.0%
1.6%
0.0%
9.0%
34.8%
9.4%
24.6%
0%
5%
10%
15%
20%
25%
30%
35%
40%
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
Cohort 89 Cohort 69 Cohort 64 Cohort 57
Cohort Analysis
Septic Shock
LOS DVC/k Readmit Mortality
$35,310.79
$17,634.29
$49,316.00
$171,135.64
$(74,951.68)
$(100,000.00)
$(50,000.00)
$-
$50,000.00
$100,000.00
$150,000.00
$200,000.00
Savings Booked to Date
Total Savings
$198,445.04
Pneumonia COPD CHF Septic Shock Sepsis w/o Shock
What do you need to do
Need the SQL skills to retrieve the data
What do you need to do
Bring physicians in early
What do you need to do
Recognized that it is an iterative process
What do you need to do
Work hard
What do you need to do
We can change the world!
www.HealthcareMachineLearningAI.com
#smartHIT
Michael C. Sanders, M.D.
Flagler Hospital
Michael.Sanders@FlaglerHospital.org